The impact of human mobility networks and weather conditions on COVID-19 spread (CoSMoS)

COSMOS project was awarded „The best research project of the University of Bucharest” and „The best research project in social sciences” by the University of Bucharest, in 2021

COSMOS project abstract

The World Health Organization declared the coronavirus disease 2019 (COVID19) a pandemic, on March 11, 2020, pointing to the over 118,000 cases in over 110 countries and territories around the world that time. When writing this project (June, 2020), the number of confirmed cases has been surging rapidly past 7,5 million mark, emphasizing the sustained risk of further global spread. In this convoluted context, governments in Romania and worldwide have imposed movement restrictions to reduce virus transmissions. These preventive measures caused undesirable socio-economic effects such as unemployment, migration crisis, psychological trauma, etc.

In parallel, some pundits have claimed that COVID19 case variations are to be accounted for not only by the policy interventions but also by weather conditions. However, the scientific results reported so far are inconclusive. Given this context, our project aims to extend the knowledge of how human mobility and weather conditions impact upon the COVID19 spread. Specifically, we examine the role of human-to-human virus transmission networks and travel patterns in the COVID19 outbreak in Romania. Also, we study the effects of weather conditions on the virus spread while taking stock of traveling flows, socio-economic and policy factors. We advance a novel approach that combines methods from social network science and physics. Results are expected to support authorities in managing COVID19 later stages and in handling socio-economic associated challenges.

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Funding: We acknowledge financial support from the Executive Agency for Higher Education, Research, Development and Innovation Funding (PN-III-P4-ID-PCE-2020-2828), project implementation: 36 months (January, 4, 2021 – December, 31, 2023). Project director: Marian-Gabriel Hâncean

COVID-19 in Romania

Early spread of COVID-19 in Romania

The time window between arrival and COVID-19 confirmation for the index cases. (a) For index cases, an illustration of individual time windows between the arrival to a Romanian county from Italy (or other country/Romanian county) and the COVID-19 confirmation. (b) The time-lag dynamics between arrival and COVID-19  confirmation. (source: Hâncean, M.-G., Perc, M., Lerner, J., 2020)

The first human-to-human COVID-19 transmission networks in Romania

Human-to-human COVID-19 transmission networks. (a) COVID-19 human-to-human transmission networks: 159 people (nodes) and 203 transmission ties (arrows). Colors indicate how people acquired the COVID-19 infection: nosocomial (red), intra-family (blue), workplace (magenta), green (public places) and grey/black (unknown source). Triangles mark the source of the infection in the networks (the seeds). (b) The distribution of the out-degree scores (how many people an individual infected) for all the 159 nodes included in the networks. (c) The pattern of COVID-19 transmissions, in absolute scores and by colors corresponding to the networks in (a). (d) Time-status display of the largest two transmission networks illustrated in (a) ((i) and (ii)). Horizontal lines mark temporal order (time-stamps) in days of COVID-19 infection confirmations. Visual variables (colors and shapes) have the same meaning as in (a). (source: Hâncean, M.-G., Perc, M., Lerner, J., 2020)

COVID-19 spreading patterns

Visualizations for the overall COVID-19 transmission patterns detected in the empirical data. (a) The contour plot is based on a referee-referral matrix. Individuals (referees and referrals) are arranged by their age. Colours in the plot illustrate the frequency of referee–referral nominations by age. Marginal histograms display age distributions for both referees and referrals (dark-red and red, respectively). (b) The bar plots show the frequency of referee-referral nominations by sex categories (females are indicated by green while males are indicated by red). Arrows designate who nominates whom as a disease origin. Moreover, it is illustrated the frequency of cases wherein the referral is female (male), irrespective of the referee’s sex (male, female or unknown). (c) The bar plots indicate the distribution of various types of pairs in the dataset. We also use three shapes to designate individuals embedded in the transmission dyads: triangles (referees), circles (referrals), and squares (brokers).

Visualizations for the overall COVID-19 transmission patterns detected in the empirical data. (a) The contour plot is based on a referee-referral matrix. Individuals (referees and referrals) are arranged by their age. Colours in the plot illustrate the frequency of referee–referral nominations by age. Marginal histograms display age distributions for both referees and referrals (dark-red and red, respectively). (b) The bar plots show the frequency of referee-referral nominations by sex categories (females are indicated by green while males are indicated by red). Arrows designate who nominates whom as a disease origin. Moreover, it is illustrated the frequency of cases wherein the referral is female (male), irrespective of the referee’s sex (male, female or unknown). (c) The bar plots indicate the distribution of various types of pairs in the dataset. We also use three shapes to designate individuals embedded in the transmission dyads: triangles (referees), circles (referrals), and squares (brokers).  (source: Hâncean, M-G, Lerner, J, Perc, M, Ghiță, MC, Bunaciu, DA, Stoica, AA, Mihăilă, BE, 2021)

COVID-19 worldwide

Early spread of COVID-19 worldwide

Type of travellers and modes of transportation for the first 323 individual COVID-19 cases. Images (a) and (b) are counts, while (c) and (d) are percentages. (source: Hâncean, M.-G., Slavinec, M., Perc, M., 2021)

Centrality layouts for visualizing the patterns of (a) COVID-19, (b) incoming migration and (c) inbound tourism ties among countries. Node size is proportional to out-degree centrality. Countries located more centrally in the pictures have higher centrality scores. Core-countries are marked with red. In (a), ties among core-countries are marked in red. (source: Hâncean, M.-G., Slavinec, M., Perc, M., 2021)

Visualizations of (a) the common language network, (b) the contiguity network and (c) the location on the same continent network. In (c), countries sharing the same geographical region are marked by colour (we employed Unites Nations’ region classification: America, Asia, Africa, Europe and Oceania). Also, the states included in the network are available by their ISO-alpha three codes (three-letter abbreviation). (source: Hâncean, M.-G., Slavinec, M., Perc, M., 2021)

The early spread of COVID-19 by regions (source: Marian-Gabriel Hâncean)